A fundamental task of robotics perception and planning in dynamic environments is the ability to predict future evolution of the situation around a robotic platform. For example, autonomous vehicles need to know about the positions of other cars and their future motion to plan and avoid collisions.
In robotics, critical tasks such as path planning and obstacle avoidance require the ability to predict or estimate the evolution of the environment around the robotic platform. Complex environments such as urban city traffic present significant challenges when it comes to such planning and perception. Methods for doing so play a significant role in reducing the risk of collisions, such as road accidents.
Currently, future movement predictions in semi-structured environments are usually based on assumed motion dynamics of the vehicles around the robotic platform or vehicle, for example by using a Kalman Filter. However, a common disadvantage is that these approaches often generalise the vast complexity of real world scenarios, such as busy intersections or turns, resulting in unreliable predictions. Similarly, the motion of vehicles in complex scenarios cannot usually be predicted reliably using simple motion models like linear extrapolation, especially if the prediction horizon is greater than a few seconds.
Another existing approach is to annotate the road infrastructure in the form of a semantic map by capturing and making a note of traffic rules which should indicate paths that vehicles are likely to follow. This has a benefit in that the map data can be used to extrapolate the expected motion of a car provided that a driver follows the traffic rules. However, the amount of work needed to produce such reliable maps and then to keep them updated is time consuming and heavily laborious.
It is an aim of the present invention to address one or more of the disadvantages associated with the prior art.